Glassdoor Company Reviews Scraper

Glassdoor Reviews Scraper: pros, cons and six sub-ratings per review

Give it Glassdoor company reviews URLs or plain company names and get one row per review: the summary line, the pros, the cons, the reviewer's job title and location, the overall star rating and six separate sub-ratings for things like work-life balance and pay.

Here is the part worth knowing before you plan around it. Without a Glassdoor login, only the first page of reviews is readable for each company. So this earns its keep across many companies with a few reviews each, not as a way to pull one employer's whole review history. Point it at 200 companies and you get a wide, shallow picture. Point it at one and you get a handful of rows.

🔍 What Glassdoor Reviews Scraper does

It opens each company's reviews page and reads the reviews off it. A URL carries the employer id in its -E<number>- part, so that route is exact. A name has to be looked up first, and Glassdoor's search decides which employer it means.

Each row carries both the company's own numbers and the individual review's. overallRating and reviewCount belong to the company. ratingOverall and the six sub-ratings belong to that one review. They sit next to each other, so read the field names rather than the column headings.

mode starts on sample. That is deliberate: it returns five built-in example rows instantly, free, so you can see the shape before spending anything. Nothing real happens until you switch it to live.

📥 What you give it

{
  "mode": "live",
  "companyUrls": ["https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm"],
  "maxReviews": 10
}

Send at least one of companyUrls or companyNames. Neither one gets you a BAD_INPUT row.

📤 What you get back

A real row from run zDcD1eM3TewzasS2W:

{
  "ok": true,
  "companyName": "Google",
  "employerId": "9079",
  "overallRating": 4.4,
  "reviewCount": 52694,
  "companyUrl": "https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm",
  "reviewId": "105583915",
  "ratingOverall": 4,
  "summary": "Good work-life balance, but limited upward mobility",
  "pros": "Cloud is a different animal. Still good work-life-balance, but still more hustle than people associate with Google",
  "cons": "Not much mobility upwards for non-tech Entry-Mid level",
  "advice": null,
  "jobTitle": "Program Manager",
  "employmentStatus": "REGULAR",
  "isCurrentJob": false,
  "lengthOfEmployment": 0,
  "location": "New York, NY",
  "reviewDate": "2026-09-14",
  "reviewDateTime": "2026-09-14T19:14:15.143",
  "recommends": "POSITIVE",
  "ceoApproval": "APPROVE",
  "businessOutlook": "NEUTRAL",
  "ratingWorkLifeBalance": 4,
  "ratingCultureAndValues": 5,
  "ratingCareerOpportunities": 3,
  "ratingCompensationAndBenefits": 4,
  "ratingSeniorLeadership": 3,
  "ratingDiversityAndInclusion": 4,
  "countHelpful": 0
}

🧾 Reading the output

Three kinds of row land in the dataset.

The Console's Overview table hides pros and cons, which are the whole point, and in sample mode two of its columns read blank. Download the dataset as JSON, CSV or Excel, or switch the table to all fields.

▶️ How to run it

1. Open Glassdoor Reviews Scraper and click Try for free. 2. Leave Run mode on Sample for the first run and look at the five free rows. 3. Switch Run mode to Live. 4. Paste reviews URLs into Company reviews URLs, or type names into Company names. 5. Set Max reviews per company, click Start, then download the dataset.

💰 How much does it cost?

$2.50 per 1,000 reviews. Flat on every Apify plan, no volume tiers.

You pay per review row. The free sample rows, duplicate reviews and every diagnostic row cost nothing, and a company that returns no reviews is not billed.

💡 What people use it for

• Employer-brand benchmarking across a list of competitors, taking the latest few reviews from each.
• Spotting which of the six sub-ratings drags a company's average down, then tracking it monthly.
• Pulling the pros and cons text for a recruiting pitch, so the objections are answered before they

are raised.

• Feeding recent review text into a summariser to see what has changed since the last quarter.

🚧 What it does not do

• No deep history. Page one per company is what a signed-out visitor can see, and there is no

login option here. Plan around breadth, not depth.

• No salaries, no interview reports, no job postings, no benefits pages, no photos. Reviews only.
• A name can resolve to the wrong employer. The lookup takes Glassdoor's closest match, so a

common or ambiguous name may land somewhere else. Use the reviews URL when it matters.

• A refused page ends the run. Companies queued behind it are not attempted, so split a long

list across several runs.

• Live runs are not guaranteed. Glassdoor decides whether to serve an automated visit, and when

it says no you get a BLOCKED row and no charge rather than partial data.

• Rows are a snapshot. Reviews can be edited or removed, and a company's average moves.

🧭 Which employer research scraper do you need?

❓ Questions people ask

Why did I only get three reviews? That is the first page for a signed-out visitor, and it is the honest ceiling. Add more companies rather than raising maxReviews.

Can I sign in to get more? No. There is no credential field, and there will not be one.

Where do I find the reviews URL? Search the company on Glassdoor, open its Reviews tab, and copy the address. It ends -Reviews-E<number>.htm.

Why is my first run full of example data? mode starts on sample. Switch it to live.

What happens when a company is blocked? You get an ok: false row naming it, you are not charged for it, and for BLOCKED the run stops there.

Is this legal? These are public reviews on public company pages. They are written by identifiable people, so GDPR and similar laws apply and you want a reason for collecting them. Apify's write-up on scraping and the law is a good starting point, and we are not lawyers.

🆘 If something breaks

Open the Issues tab on the actor page. Send the company URL or name and the run id. The errorCode on the diagnostic row usually names the problem by itself.